Method for editing propagation of video and image content based on local feature structure preservation

ABSTRACT

The invention discloses a method for editing propagation of video and image content based on local feature structure preservation, comprising: mapping all pixels in the input original image and/or video key frames to a selected feature space; finding K nearest neighbor pixels for each pixel according to feature vectors&#39; Euclidean distance in the selected feature space; using Locally Linear Embedding (LLE) dimension reduction to construct the locally linear relationship between each pixel and its K nearest neighbor pixels in the selected feature space; According to the present invention, it is possible to accurately perform such image or video processing as automatic color transformation, interactive color editing, gray image colorization, video cloning and image matting.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the priority benefit of Chinese patentapplication No. 201210331180.2, filed on Sep. 7, 2012. The entirety ofthe above-mentioned patent application is hereby incorporated byreference herein and made a part of specification.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to the field of image processing, computervision and augmented processing technology, and in particular to themethod for editing propagation of video and image content based on localfeature structure preservation.

2. Background of the Invention

Editing based on the video and image contents is among the most commontechnology for image processing in the field of digital imageprocessing. Example of editing based on the video and image contents maycomprise, for example, changing color of the video and image (colortransformation), merging objects from different video sources to form avideo without editing traces (seamless cloning), extracting accuratelyhairs from an image (matting) or the like. For the art designers andvideo editors, it requires a lot of manual editions to edit the colorand content of a video. In fact, there are some intrinsic relationshipsamong content features of the video and image. In case that the videoand image can be edited automatically according to these intrinsicrelationships, it is possible to increase dramatically the efficiencyfor editing video and image.

Researches about image editing have been conducted widely. Zeev Farbmanet al. proposed in 2010 an editing propagation method based on thediffusion map, in which the diffusion distance is used to measureaffinity among all pixels. This method is neither efficient, nor caneffectively reflect the non-affinity among pixels. In addition, thismethod is not appropriate for processing pixels in the color transitionregion.

As for color transformation, Eric Reinhard et al. in the University ofBristol firstly proposed a global method for color transformation in2001. In this method, the target image and reference image are firstlyconverted from the RGB color space to the LAB color space. Then, theexpectations and standard deviations along each axis of LAB color spaceare calculated, each pixel in the target image is scaled and shifted,and finally each pixel value is transformed back to the RGB color space.Although this method is simple and effective, the user is required tospecify the reference for color transformation in case of a compleximage.

As for cloning, Perez et al. proposed in 2003 a method for merging thescene and object based on the Poisson equation and the Dirichletboundary conditions. Although the inserted object can be mergedappropriately, this method consumes time and space. In 2009, ZeevFarbman et al. presented an image and video cloning method based onmean-value coordinates, which greatly improves the time and spaceconsumption of the Poisson method. However, the mean-value cloning issusceptible to the shape of the inserted object.

Matting was firstly proposed by Jian Sun et al. in 2004. This methodfollowed the principle of the Poisson equation to conduct the task ofmatting. However, this method suffers from low calculation speed andlarge consumption of storage space, and cannot extract the foregroundwell in the semi-transparent image region. Ahat Levin et al. proposed aspectral matting in 2008. Although this method improves to a certainextent the accuracy of matting, it still cannot extract the foregroundwell in the semi-transparent image region.

As for colorization of gray images, Welsh et al. firstly presented in2001 a method for colorizing gray images based on gray matching. In thismethod, it is necessary to provide a color image which is similar to thescene of the gray image, and the gray image is colorized according tothe gray matching between these two images. However, a gray image withcomplex scene cannot be colorized well by this method, and too muchinteraction may be involved during colorization.

BRIEF SUMMARY OF THE INVENTION

According to the practical requirement and the key problems, theinvention aims to propose a robust and adaptive locally linear featuremanifold structure preserving method to edit the user scribbles to allvideo key frames, no matter what shapes of the objects are. To achievethe above object or some other objects, according to the presentinvention, the method for editing propagation of video and image contentbased on local feature structure preservation may comprise:

-   -   Step S100, mapping all pixels in the input original image and/or        video key frames to a selected feature space;    -   Step S200, finding K nearest neighbor pixels for each pixel        according to feature vectors' Euclidean distance in the selected        feature space;    -   Step S300, using Locally Linear Embedding (LLE) dimension        reduction to construct the locally linear relationship between        each pixel and its K nearest neighbor pixels in the selected        feature space;    -   Step S400, mapping the user specified editing requests on the        input original image and/or video key frames to all or a part of        pixels in the input original image and/or video key frames in        the selected feature space;    -   Step S500, according to the locally linear relationship in Step        S300 and by means of the resulting editing requests in Step        S400, propagating the user specified editing requests to all        other pixels in the input original image and/or video key        frames;    -   Step S600, reversely mapping all pixels which have been modified        by propagation in the selected feature space to the input        original image and/or video key frames, and replacing the        corresponding pixels in the input original image and/or video        key frames, so as to generate the resulting image and/or video        key frames.

Preferably, during mapping all pixels in the input original image and/orvideo key frames to the selected feature space in step S100, the featurespace is selected according to the specified different applicationrequirements, including automatic color transformation, interactivecolor editing, gray image colorization, video cloning and image matting.

For automatic color transformation and interactive video objectsrecoloring, the selected feature space is a RGB color space.

For video cloning and image matting, we define a RGBXYT six-dimensionalfeature space as the concatenated RGB color and spatial temporalcoordinate (x, y, t). Here (x, y) is the spatial coordinates and t isthe video frame index.

For gray image colorization, the selected feature space concatenates thegrayscale intensity, texture, and SIFT features.

Preferably, finding K nearest neighbors for each pixel in the givenfeature space in step S200 is to find its K nearest neighbors in theselected feature space for each pixel.

For automatic color transformation and interactive video objectsrecoloring, the K nearest neighbors are the neighbors with minimum colordistance in RGB space.

For video cloning and image matting, the K nearest neighbors are theneighbors with minimum color and spatial distance.

For gray image colorization, the K nearest neighbors are the closest inthe Intensity-SIFT-Texture-Coordinate feature space.

The distance is a Euclidean distance.

Preferably, using Locally Linear Embedding (LLE) dimension reduction toconstruct the locally linear relationship between each pixel and its Knearest neighbor pixels in the selected feature space in step S300 maycomprise:

-   -   finding K nearest neighbors with minimum Euclidean distance for        each pixel in the given feature space; and    -   computing a set of weights that can best reconstruct the current        pixel from its K neighbors.

Preferably, as an embodiment, the set of weight coefficients that bestlinearly reconstruct the pixel from the K nearest neighbors can becomputed by constructing a matrix of the sum of squared differencesbetween the pixel and its K neighbors and solving the optimizationequation with the least square method.

Preferably, suppose a vector X_(i) to represent a pixel i in somefeature space. For X_(i), we find its K nearest neighbors, namelyX_(i1); . . . ; X_(ik). We compute a set of weights W_(ij) that can bestreconstruct X_(i) from these K neighbors. Specifically, we computeW_(ij) by minimizing

${\min{\sum\limits_{i = 1}^{N}\;{{X_{i} - {\sum\limits_{j = 1}^{K}\;{w_{ij}X_{ij}}}}}^{2}}},$

which is subject to the constraint

${\sum\limits_{j = 1}^{K}\; w_{ij}} = 1.$The result matrix {w_(ij)|i=1, . . . , N} captures the manifoldstructure of this data set in the feature space.

Suppose the user specifies the results g_(i) for a subset of pixels S.The algorithm can propagate this editing to the whole image by inferringa value z_(i) at each pixel by minimizing the following energy

$E = {{\lambda{\sum\limits_{i \in S}\;\left( {z_{i} - g_{i}} \right)^{2}}} + {\sum\limits_{i = 1}^{N}\;{\left( {z_{i} - {\sum\limits_{z_{j} \in N_{i}}\;{w_{ij}z_{j}}}} \right)^{2}.}}}$Here, z_(i) is the edited result at pixel i.

Preferably, mapping the user specified editing requests on the inputoriginal image and/or video key frames to all or a part of pixels in theinput original image and/or video key frames in the selected featurespace in step S400 may comprise:

-   -   mapping the target color theme to the source color theme of the        source image or video according to both color theme distance for        automatic color transformation;    -   mapping the user specified colors to the corresponding pixels'        colors in the key frame for interactive color editing;    -   according to the given tri-map, getting the definite foreground        region, definite background region and the uncertain region for        image matting;    -   according to the user specified source region in the source        image or video and the selected location to insert the user        specified region in the target image or video, constructing the        mapping between the pixels set in boundary of the source region        and that in the target image or video for video cloning;    -   mapping the colors to the corresponding super-pixels according        to the user scribbles.

Preferably, the step 500 may comprise:

-   -   propagating the color theme to all pixels in the image or video        for the application of automatic color transformation.    -   propagating the user specified color to all other pixels in the        image or video for the application of the interactive color        editing;    -   propagating the probability to the pixels in the uncertain        regions to obtain the probability of the pixel belongs to the        foreground region;    -   propagating the colors difference of the pixels colors in the        source boundary with the pixels colors in the corresponding        location of the target image or video to the inner region of the        insert source region for video cloning, wherein the final color        of each pixel in the insert region is the sum of the source        color and the obtained difference after propagation;    -   propagating the user scribbles colors to all super pixels        according to the locally linear manifold structure of super        pixels for the application of gray image colorization.

Compared with existing technologies, the invention has followingadvantages. Firstly, the invention gives a method which can be appliedto various applications, such as automatic color transformation,interactive color editing, gray image colorization, video cloning andimage matting. Secondly, the framework proposed by the invention issimple and effective and does not require a lot of manual editing orprofessional skills. User will be able to get the desired results in ashort period of time with just a little rough interaction, which iseffective and time-saving. Thirdly, the invention effectively solves thelocally linear manifold structure problem in the color transition areain the video and image while avoids artifacts in these regions.Fourthly, the method proposed by the invention can protect the image andvideo editing propagation from the influence of the shape of the objectwith a certain degree of adaptability and robustness.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is the flowchart of the method for editing propagation of videoand image content based on local feature structure preservationaccording to the present invention;

FIG. 2 (a)-FIG. 2 (c) are schematic diagrams showing the principles ofthe present invention;

FIG. 3 is the flowchart showing automatic color transformation of thepresent invention;

FIG. 4 is the flowchart showing interactive color editing of the presentinvention;

FIG. 5 is the flowchart showing video cloning of the present invention;

FIG. 6 is the flowchart showing image matting of the present invention;

FIG. 7 is the flowchart showing gray image colorization of the presentinvention.

DETAILED DESCRIPTION OF THE EMBODIMENTS

In order to make the objects, technical solutions and advantages of thepresent invention clearer, the method for editing propagation of videoand image content based on local feature structure preservation of thepresent invention will be explained hereinafter with reference to theaccompanying drawings and embodiments. It should be understood that thespecific examples described herein only intend to explain the presentinvention and by no means to limit the scope of the present invention.

The present invention proposes a framework which can preserve localfeatures manifold structure in video and image while avoiding artifactsduring video and image processing. At the same time, the framework isapplied to five typical applications such as automatic colortransformation, interactive color editing, image matting, video cloningand gray image colorization.

The local features manifold structure preserving for editing propagationin video and image method of the present invention has the followingadvantages: For the areas of color transition in video and image, themethod can preserve the local features manifold structure between thepixels in these areas during video and image editing process whileavoiding problems in these areas such as color division and coloroverflow and ensure the color of the generated result transits smoothly.

The present invention proposes a method for editing propagation of videoand image content based on local feature structure preservation. Asshown in FIG. 1, the method consists of the following steps:

-   -   Step S100, mapping all pixels in the input original image and/or        video key frames to a selected feature space.

To map all pixels in the image or video to the selected feature space,as an embodiment, is to select the feature space according to thecorresponding different application requirements, including automaticcolor transformation, interactive color editing, gray imagecolorization, video cloning and image matting.

Specifically, for automatic color transformation and interactive videoobjects recoloring, the selected feature space is a RGB color space.

For video cloning and image matting, we define a six-dimensional RGBXYTfeature space as the concatenated RGB color and spatial temporalcoordinates (x, y, t). Here (x, y) is the spatial coordinate and t isthe video frame index.

For gray image colorization, the selected feature space isIntensity-SIFT-Texture-Coordinate.

-   -   Step S200, finding K nearest neighbor pixels for each pixel        according to feature vectors' Euclidean distance in the selected        feature space.

As an embodiment, to find K nearest neighbors for each pixel in thegiven feature space is to find its K nearest neighbors in the selectedfeature space for each pixel.

Specifically, for automatic color transformation and interactive videoobjects recoloring, the K nearest neighbors are the neighbors withminimum color distance in the RGB space.

For video cloning and image matting, the K nearest neighbors are theneighbors with minimum color and spatial distance.

For gray image colorization, the K nearest neighbors are closest ones inthe Intensity-SIFT-Texture-Coordinate feature space.

Herein the distance is a Euclidean distance.

-   -   Step S300, using Locally Linear Embedding (LLE) dimension        reduction to construct the locally linear relationship between        each pixel and its K nearest neighbor pixels in the selected        feature space.

Using LLE to represent each pixel as a linear combination of itsneighbors in a feature space and constructing the manifold structure forall pixels is to calculate the relationship between all pixels in thefeature space. The relationship substantially reflects the similarityand distinguishability between pixels. Hence, for the colors in thetransition area, in order to preserve the relationship between them anddistinguishability from colors in other regions, the more K nearestneighbors, the better the local features manifold structure for editingpropagation in video and image is preserved.

Preferably, as an embodiment, using LLE dimension reduction to constructthe locally linear relationship between each pixel and its K nearestneighbor pixels in the selected feature space in step S300 may comprise:

-   -   finding K nearest neighbors with minimum Euclidean distance for        each pixel in the given feature space;    -   computing a set of weights which can best reconstruct the        current pixel from its K neighbors.

Preferably, as an embodiment, the set of weight coefficients that bestlinearly reconstruct the pixel from the K nearest neighbors can becomputed by constructing a matrix of the sum of squared differencesbetween the pixel and its K neighbors and solving the optimizationequation with the least square method.

Preferably, suppose a vector X_(i) to represent a pixel i in somefeature space. For X_(i), we find its K nearest neighbors, namelyX_(i1); . . . ; X_(ik). We compute a set of weights w_(ij) that can bestreconstruct X_(i) from these K neighbors. Specifically, we computeW_(ij) by minimizing

${\min{\sum\limits_{i = 1}^{N}\;{{X_{i} - {\sum\limits_{j = 1}^{K}\;{w_{ij}X_{ij}}}}}^{2}}},$

which is subject to the constraint

${\sum\limits_{j = 1}^{K}\; w_{ij}} = 1.$The result matrix {w_(ij)|i=1, . . . , N} captures the manifoldstructure of this data set in the feature space.

Suppose the user specifies the results g_(i) for a subset of pixels S.The algorithm can propagate this editing to the whole image by inferringa value z_(i) at each pixel by minimizing the following energy:

$E = {{\lambda{\sum\limits_{i \in S}\;\left( {z_{i} - g_{i}} \right)^{2}}} + {\sum\limits_{i = 1}^{N}\;{\left( {z_{i} - {\sum\limits_{z_{j} \in N_{i}}\;{w_{ij}z_{j}}}} \right)^{2}.}}}$

Here, z_(i) is the edited result at pixel i.

The step S300 which preserve the local features manifold structurebetween the pixels in color transition areas during video and imageediting process can avoid problems in these areas, such as colordivision and color overflow.

-   -   Step S400, mapping the user specified editing requests on the        input original image and/or video key frames to all or a part of        pixels in the input original image and/or video key frames in        the selected feature space.

In order to apply the local features manifold structure preservingframework to different applications, the user specified editing requestsare different.

Specifically, for video objects recoloring, the user specified editingrequests are to specify new color for some objects or regions, label newcolors to some pixels. Hence, we can map the labeled pixels' color tothe colors that user specified.

For image matting, the user specified editing requests are to classifythe regions into foreground, background and uncertain. Hence, we can mapthe pixels color to foreground, background or uncertain region.

For video cloning, the user specified editing requests is the spatialposition the object will be inserted. According to the position, we canmap the pixels on the boundary of the object to the pixels in the targetimage overlaid by the boundary.

For gray image colorization, the user specified editing requests are tospecify new color for some objects or regions, and label new colors tosome pixels.

Preferably, mapping the user specified editing requests on the inputoriginal image and/or video key frames to all or a part of pixels in theinput original image and/or video key frames in the selected featurespace in step S400 may comprise:

-   -   mapping the target color theme to the source color theme of the        source image or video according to both color theme distance for        automatic color transformation.

Preferably, as an embodiment, the color theme can be specified manuallyor extracted from other video and image according to eleven basiccolors.

As an embodiment, the source color theme extraction clusters theoriginal colors with eleven basic colors or using K-means clustering,and then computes the mean color for each type to generate the colortheme.

-   -   mapping the user specified colors to the corresponding pixels'        colors in the key frame for interactive color editing;    -   according to the given tri-map, getting the definite foreground        region, definite back-ground region and the uncertain region for        image matting;    -   according to the user specified source region in the source        image or video and the selected location to insert the user        specified region in the target image or video, constructing the        mapping between the pixels set in boundary of the source region        and that in the target image or video for video cloning.

Preferably, as an embodiment, the video cloning uses LLE to representeach pixel as a linear combination of pixels on the boundary to achievecolor interpolation.

-   -   mapping the colors to the corresponding super-pixels according        to the user scribbles;    -   propagating the specified editing requests to all other pixels        based on the step S400 while maintaining the manifold structure        in the result image or video.

The specified editing requests are propagated to all other pixels, whilethe manifold structure in the result image or video is maintained. Inmore detail, according to the specified user editing requests such asautomatic color transformation, interactive color editing, imagematting, video cloning and gray image colorization, manifold structurebetween pixels and user specified editing requests or mappings, the userediting requests are propagated to all other pixels.

Specifically, as an embodiment, for automatic color transformation andinteractive video objects recoloring, the user specified color or colortheme is propagated to all other pixels.

For image matting, the probability of foreground of each other pixels bypropagating the specified foreground and background pixels iscalculated.

For video cloning, the color difference along the boundary to the pixelsin the object is propagated.

For gray image colorization, the specified color to other super pixelsbased on the relationship between gray image and its super pixels ispropagated.

Preferably, propagating the specified editing requests to all otherpixels based on the step S400 while maintaining the manifold structurein the result image or video in step 500 may comprise:

-   -   propagating the color theme to all pixels in the image or video        for the application of automatic color transformation;    -   propagating the user specified color to all other pixels in the        image or video for the application of the interactive color        editing;    -   propagating the probability to the pixels in the uncertain        regions to obtain the probability of the pixel belongs to the        foreground region according to the tri-map;    -   propagating the colors difference of the pixels colors in the        source boundary with the pixels colors in the corresponding        location of the target image or video to the inner region of the        insert source region for video cloning, wherein he final color        of each pixel in the insert region is the sum of the source        color and the obtained difference after propagation;    -   propagating the user scribbles colors to all super pixels        according to the locally linear manifold structure of super        pixels for the application of gray image colorization;    -   optimizing the results by processing the artifacts such as color        overflow, local smooth of patches.

The following examples illustrate the local features manifold structurepreserving for editing propagation in video and image method of thepresent invention.

Let's take interactive color editing as example. As shown in FIG. 2, thepixel A in FIG. 2 (a) is in the color transition region, while the pixelB and pixel C in FIG. 2 (a) are in regions whose color are consistent.

FIG. 2 (b) is the distribution of pixels of FIG. 2( a) in the LAB colorspace. It is clear that pixel A is in the transition region betweenpixel B and pixel C.

FIG. 2 (c) is the result generated by the manifold structure preservingmethod of the present invention. It indicates that the method of thepresent invention has advantages in processing video and image withcolor transition regions compared with existing technologies. Overall,the method for editing propagation of video and image content based onlocal feature structure preservation of the present invention can bedivided into two steps: (1) constructing local features manifoldstructure; and (2) propagating the specified editing requests to allother pixels in the result video and image.

FIG. 3 is the flowchart showing automatic color transformation of thepresent invention. The embodiment extracts the color theme from thesource video, then maps it to the specified color theme and generatesthe result. The color theme of the video or image is a set of colorsthat can represent the color style of the video or image.

As an embodiment, preferably, calculating the color theme may comprisethe following steps:

firstly, classifying the pixels into several colors by K-meansclustering and computing the count of each color;

then, computing the sum of each color in RGB color space; and

finally, computing the mean color for each color as one basic color ofthe color theme.

The optimized mapping relationship is obtained by computing the sum ofEuclidean distance between the two color themes.

FIG. 4 is the flowchart of interactive color editing which aimed tomodify the color of the video or image under the guidance of specifiedcolors to certain regions or objects in the key frame. In thisembodiment, the user draws color scribbles to specify the desired colorsat some sample pixels and uses black strokes to enforce pixel colors tobe unchanged. Then, under the constraint of user editing requests, theresult is generated by propagating the editing requests to all otherpixels according to the local features manifold structure.

FIG. 5 is the flowchart showing video cloning which aims to seamlesslyclone the object into a target scene and ensure the visualreasonableness of the result.

As an embodiment, the seamless cloning may comprise the following steps:

constructing adaptive triangular mesh over the selected patch, whereinthe patch can be selected by stroke or be selected using specified mask;

constructing the local features manifold structure between each meshvertex and pixels on the boundary;

computing the color difference between pixels on the source patchboundary and corresponding pixels overlaid by them in the target image,and then generating the interpolation value of each mesh vertex.

The value at each pixel is obtained by linear interpolation of threevalues at the vertices of the containing triangle. The interpolatedmembrane is added to the cloned patch to get the final result.

FIG. 6 is the flowchart showing image matting. Firstly, the pixels areclassified into three classes: foreground, background and uncertain withsource image and tri-map. Then, the local features manifold structurebetween uncertain pixels and known (foreground and background) pixels isconstructed. Finally, the result is generated by computing theprobability of each uncertain pixel.

FIG. 7 is the flowchart showing gray image colorization. Firstly, thefeature of each super pixel and construct the local features manifoldstructure are extracted. Then, all other super pixels' color is computedaccording to user specified super pixel's color. Finally, the grayscaleintensity is taken as the Y channel in the YUV color space and the finalresult is generated after smooth processing.

The present invention proposes a method for editing propagation of videoand image content based on local feature structure preservation. Theframework is simple and easy to implement with high time-spaceefficiency. The invention proposes and implements the local featuresmanifold structure preserving method which solves the problem of colortransition regions and improves the realism of the result in video andimage processing. At the same time, the invention has a high automationand reduces user interaction. The invention designs and implements fiveapplications such as automatic color transformation, interactive colorediting, image matting, video cloning and gray image colorization basedon the local features manifold structure preserving for editingpropagation in video and image method, which also proves goodscalability of the method.

The present invention proposes a framework which can preserve localfeatures manifold structure in video and image while avoiding artifactsduring video and image processing. At the same time, the framework isapplied to five typical applications such as automatic colortransformation, interactive color editing, image matting, video cloningand gray image colorization.

The local features manifold structure preserving for editing propagationin video and image method of the present invention has the followingadvantages. For the areas of color transition in video and image, themethod can preserve the local features manifold structure between thepixels in these areas during video and image editing process whileavoiding problems in these areas such as color division and coloroverflow, and can ensure that the color of the generated result transitssmoothly.

It should be noted that, the technicians in the art obviously can makevarious modifications and variations to the present invention withoutdeparting from the spirit and scope of the present invention. Hence, anyequivalent modification regarding the structure or the process flow onthe basis of present description and drawings, whether with direct orindirect application to other relevant technical fields, are consideredto fall within the scope of protection of the present invention.

What is claimed is:
 1. A method for editing propagation of video andimage content based on local feature structure preservation, comprising:Step S100, mapping all pixels in an input original image and/or videokey frames to a selected feature space; Step S200, finding K nearestneighbor pixels for each pixel according to feature vectors' Euclideandistance in the selected feature space; Step S300, using Locally LinearEmbedding (LLE) dimension reduction to construct a locally linearrelationship between each pixel and its K nearest neighbor pixels in theselected feature space; Step S400, mapping user specified editingrequests on the input original image and/or video key frames to all or apart of pixels in the input original image and/or video key frames inthe selected feature space; Step S500, according to the locally linearrelationship in Step S300 and by means of the resulting editing requestsin Step S400, propagating the user specified editing requests to allother pixels in the input original image and/or video key frames; andStep S600, reversely mapping all pixels which have been modified bypropagation in the selected feature space to the input original imageand/or video key frames, and replacing the corresponding pixels in theinput original image and/or video key frames, so as to generate aresulting image and/or video key frames.
 2. The method for editingpropagation of video and image content based on local feature structurepreservation of claim 1, wherein in the selected feature space, pixelscluster according to the similarity of their feature vectors, that is,the smaller the Euclidean distance between two pixels is, the moresimilar the feature vectors of two pixels is, and vice versa.
 3. Themethod for editing propagation of video and image content based on localfeature structure preservation of claim 2, wherein LLE dimensionreduction in Step S300 comprises: for any pixel i, supposing a vectorX_(i) to represent the feature vector of the pixel i, X_(i1); . . . ;X_(ik) to represent the feature vectors of K nearest neighbor pixels ofX_(i), and N is the total number of all pixels in the feature space, alinear combination coefficient W_(ij) is constructed as:${\min{\sum\limits_{i = 1}^{N}\;{{X_{i} - {\sum\limits_{j = 1}^{K}\;{w_{ij}X_{ij}}}}}^{2}}},$namely, calculating squares of a module of difference between eachpixel's feature vector and its K nearest neighbor pixels' linearcombination, and minimizing the sum of the squares by using the leastsquare method, so as to generate the optimal linear combinationcoefficient W_(ij) between each pixel and each of its K nearest neighborpixels, wherein the linear combination coefficient corresponds to thelocally linear relationship in Step S300.
 4. The method for editingpropagation of video and image content based on local feature structurepreservation of claim 3, wherein propagating the user specified editingrequests in Step S500 is achieved by minimizing an energy equation:${E = {{\lambda{\sum\limits_{i \in S}\;\left( {z_{i} - g_{i}} \right)^{2}}} + {\sum\limits_{i = 1}^{N}\;\left( {z_{i} - {\sum\limits_{z_{j} \in N_{i}}\;{w_{ij}z_{j}}}} \right)^{2}}}},$wherein E is the energy, λ is an adjustable parameter, S is a set ofpixels that the user specified editing requests covers, g_(i) is theuser specified editing requests for pixel i, Z_(i) is the editingpropagation result of pixel i, N_(i) is the set of K nearest neighborpixels of pixel i, and Z_(ij) is the editing propagation result of thej-th neighbor pixel of pixel i, wherein the equation is solved byminimizing the value of E, thus generating the optimal Z_(i) of eachpixel to complete the propagation.
 5. The method for editing propagationof video and image content based on local feature structure preservationof claim 4, wherein the specified editing requests aim at automaticcolor transformation or interactive color editing, the components of thefeature vector of a pixel are RGB color values, the selected featurespace is a RGB color space; and wherein in the feature space, the pixelscluster according to the similarity of their RGB values, and a selectedK nearest neighbor pixels are those K pixels which have the most similarcolor values.
 6. The method for editing propagation of video and imagecontent based on local feature structure preservation of claim 4,wherein the specified editing requests aim at image and video matting orcloning, the components of the feature vector are RGBXYT values, whereinRGB indicates color value, XY indicates the spatial coordinates ofpixels, and T indicates an index of the key frames, and the selectedfeature space is a RGBXYT six dimensional space; and wherein in thefeature space, the pixels cluster according to the similarity of theirRGB values and spatial coordinates, a selected K nearest neighbor pixelsare those K pixels which have the most similar color values and spatialcoordinates, and the feature vectors are compared in term of the colorvalues, spatial distances, and T values.
 7. The method for editingpropagation of video and image content based on local feature structurepreservation of claim 4, wherein the specified editing requests aim atgray image colorization, the components of the feature vector aregrayscale values, SIFT, texture and spatial coordinates, the selectedfeature space is a grayscale-SIFT-texture-spatial coordinates space; andwherein in the feature space, the pixels cluster according to thesimilarity of their grayscale value, SIFT, texture and spatialcoordinates, a selected K nearest neighbor pixels are those K pixelswhich have the most similar grayscale value, SIFT, texture and spatialcoordinates, and the feature vectors are compared in term of thegrayscale value, SIFT, texture and spatial coordinates.
 8. The methodfor editing propagation of video and image content based on localfeature structure preservation of claim 2, wherein mapping the userspecified editing requests to the corresponding pixels in the inputoriginal image and/or video key frames in the selected feature space inStep S400 comprises: mapping the user specified editing requests forautomatic color transformation to a color theme to be modified; ormapping the user specified editing requests for interactive colorediting to specified pixels in the original input image and/or video keyframes so they can be replaced with specified colors; or mapping theuser specified editing requests for image matting to pixels of a givenblack-white-gray tri-map; or mapping the user specified editing requestsfor video cloning to pixels of a selected cloning region; or mapping theuser specified editing requests for gray image colorization to pixels inregions with specified tones.
 9. The method for editing propagation ofvideo and image content based on local feature structure preservation ofclaim 2, wherein propagating the specified editing requests in step S500comprises: for editing requests for automatic color transformation,propagating a specified color to other pixels for changing colorthereof; or for editing requests for interactive color editing,propagating a user's specified color to other pixels for changing colorthereof; or for editing requests for image matting, according to a whiteforeground and black background determined by a black-white-graytri-map, generating a probability that a pixel of a gray region belongsto the foreground region; or for editing requests for video cloning,according to color errors between a boundary of an inserted region and atarget background, changing a color of the inserted region; or forediting requests for gray image colorization, colorizing all pixelsaccording to a super pixels' manifold structure.
 10. The method forediting propagation of video and image content based on local featurestructure preservation of claim 1, wherein after propagating the userspecified editing requests to all other pixels in the input originalimage and/or video key frames in Step S500, the method furthercomprises: handling exceptions in which the propagated pixel(s) has afeature value beyond a limit.
 11. The method for editing propagation ofvideo and image content based on local feature structure preservation ofclaim 10, wherein said handling the exceptions comprises: processing thefeature value(s) whose color is abnormal; processing the featurevalue(s) whose texture is abnormal; processing the feature value(s)whose spatial coordinates are abnormal; and/or processing the featurevalue(s) of boundary pixels, and then generating final propagationresults.
 12. The method for editing propagation of video and imagecontent based on local feature structure preservation of claim 11,wherein processing the feature value(s) of boundary pixels comprises:obtaining the color differences between the boundary pixels and thepixels which are near the boundary pixels in the original input imageand/or video key frames, and modifying a propagation relationship ofrelevant pixels.